Out[2]:
City Date PM2.5 PM10 NO NO2 NOx NH3 CO SO2 O3 Benzene Toluene Xylene AQI AQI_Bucket
0 Ahmedabad 2015-01-01 NaN NaN 0.92 18.22 17.15 NaN 0.92 27.64 133.36 0.00 0.02 0.00 NaN NaN
1 Ahmedabad 2015-01-02 NaN NaN 0.97 15.69 16.46 NaN 0.97 24.55 34.06 3.68 5.50 3.77 NaN NaN
2 Ahmedabad 2015-01-03 NaN NaN 17.40 19.30 29.70 NaN 17.40 29.07 30.70 6.80 16.40 2.25 NaN NaN
3 Ahmedabad 2015-01-04 NaN NaN 1.70 18.48 17.97 NaN 1.70 18.59 36.08 4.43 10.14 1.00 NaN NaN
4 Ahmedabad 2015-01-05 NaN NaN 22.10 21.42 37.76 NaN 22.10 39.33 39.31 7.01 18.89 2.78 NaN NaN
Out[3]:
City              0
Date              0
PM2.5          4598
PM10          11140
NO             3582
NO2            3585
NOx            4185
NH3           10328
CO             2059
SO2            3854
O3             4022
Benzene        5623
Toluene        8041
Xylene        18109
AQI            4681
AQI_Bucket     4681
dtype: int64
Out[4]:
(29531, 16)
Out[8]:
City             0
PM2.5            0
PM10             0
NO               0
NO2              0
NOx              0
NH3              0
CO               0
SO2              0
O3               0
Benzene          0
Toluene          0
Xylene           0
AQI              0
AQI_Bucket    4681
Year             0
Month            0
Day              0
dtype: int64
C:\Users\Vinit Solanki\AppData\Local\Temp\ipykernel_14364\1682559575.py:1: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  df['AQI'].fillna(method='ffill', inplace=True)
C:\Users\Vinit Solanki\AppData\Local\Temp\ipykernel_14364\1682559575.py:2: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  df['AQI_Bucket'].fillna(method='ffill', inplace=True)
Out[11]:
City PM2.5 PM10 NO NO2 NOx NH3 CO SO2 O3 Benzene Toluene Xylene AQI AQI_Bucket Year Month Day
0 Ahmedabad 48.57 95.68 0.92 18.22 17.15 15.85 0.92 25.39 75.715 0.00 0.02 0.98 118.0 NaN 2015 1 1
1 Ahmedabad 48.57 95.68 0.97 15.69 16.46 15.85 0.97 24.55 34.060 3.68 5.50 0.98 118.0 NaN 2015 1 2
2 Ahmedabad 48.57 95.68 17.40 19.30 29.70 15.85 2.64 25.39 30.700 5.69 13.13 0.98 118.0 NaN 2015 1 3
3 Ahmedabad 48.57 95.68 1.70 18.48 17.97 15.85 1.70 18.59 36.080 4.43 10.14 0.98 118.0 NaN 2015 1 4
4 Ahmedabad 48.57 95.68 22.10 21.42 37.76 15.85 2.64 25.39 39.310 5.69 13.13 0.98 118.0 NaN 2015 1 5
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
29526 Visakhapatnam 15.02 50.94 7.68 25.06 19.54 12.47 0.47 8.55 23.300 2.24 12.07 0.98 41.0 Good 2020 6 27
29527 Visakhapatnam 24.38 74.09 3.42 26.06 16.53 11.99 0.52 12.72 30.140 0.74 2.21 0.98 70.0 Satisfactory 2020 6 28
29528 Visakhapatnam 22.91 65.73 3.45 29.53 18.33 10.71 0.48 8.42 30.960 0.01 0.01 0.98 68.0 Satisfactory 2020 6 29
29529 Visakhapatnam 16.64 49.97 4.05 29.26 18.80 10.03 0.52 9.84 28.300 0.00 0.00 0.98 54.0 Satisfactory 2020 6 30
29530 Visakhapatnam 15.00 66.00 0.40 26.85 14.05 5.20 0.59 2.10 17.050 1.07 2.97 0.98 50.0 Good 2020 7 1

29531 rows × 18 columns

Preprocessing complete. Cleaned dataset saved.
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Model Training, Testing and Evaluation

Out[24]:
City          0
PM2.5         0
PM10          0
NO            0
NO2           0
NOx           0
NH3           0
CO            0
SO2           0
O3            0
Benzene       0
Toluene       0
Xylene        0
AQI           0
AQI_Bucket    0
Year          0
Month         0
Day           0
dtype: int64
Out[27]:
LinearRegression()
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LinearRegression()
Model Coefficients: [ 0.66259842  0.0626614   0.02131187  0.0307719   0.02781581 -0.02218568
  0.22053025  0.07155377  0.02452041 -0.02309724  0.05487827  0.        ]
Intercept: -0.0007202162324249466

       Actual AQI  Predicted AQI
22593   -0.327402      -0.526979
22459   -0.327402      -0.526979
24213   -0.097410      -0.069059
25301    0.544147       0.720180
20886    2.063305       1.556177

Mean Absolute Error (MAE): 0.3165791689386951
Mean Squared Error (MSE): 0.2059085100144702
Root Mean Squared Error (RMSE): 0.4537714292619911
R-Squared (R²): 0.7912012234170357
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        Date       City     PM2.5      PM10        NO       NO2       NOx  \
0 2015-01-04  10.320000  0.087282  0.203731 -0.231066 -0.342167 -0.222933   
1 2015-01-11  10.714286  0.098252  0.204767  0.222317  0.187937  0.021110   
2 2015-01-18  10.714286  0.098252  0.204767  0.237162  0.042651  0.125678   
3 2015-01-25  10.714286  0.095815  0.191041 -0.064450 -0.263890 -0.299929   
4 2015-02-01  10.714286  0.225171  0.189906  0.054169 -0.111199 -0.081368   

        NH3        CO       SO2        O3   Benzene   Toluene  Xylene  \
0 -0.112833  0.676313  0.462941 -0.417322  0.062761  0.300881     0.0   
1  0.142625  0.638029  0.446622 -0.385076  0.329840  0.345271     0.0   
2  0.063202  0.747507  0.321056 -0.275442  0.404165  0.476483     0.0   
3 -0.118319  0.967285  0.131151 -0.228494  0.250962  0.020355     0.0   
4 -0.094327  0.730538 -0.052651  0.031742  0.187905 -0.052356     0.0   

        AQI  AQI_Bucket    Year     Month        Day  
0 -0.028413    2.960000  2015.0  1.000000   2.560000  
1  0.014127    2.857143  2015.0  1.000000   8.000000  
2  0.014127    2.857143  2015.0  1.000000  15.000000  
3 -0.001065    2.795918  2015.0  1.000000  22.000000  
4  0.172108    2.551020  2015.0  1.142857  24.571429  

   Year  Month       City     PM2.5      PM10        NO       NO2       NOx  \
0  2015      1  10.668224  0.114191  0.198100  0.062805 -0.083797 -0.090199   
1  2015      2  10.714286  0.288594  0.183243 -0.052974 -0.012055  0.004397   
2  2015      3  10.714286  0.206257  0.062114 -0.124321 -0.106982  0.008914   
3  2015      4  10.714286  0.178300  0.135780 -0.149716 -0.146632 -0.072492   
4  2015      5  10.714286  0.093103  0.204767 -0.185286 -0.084963 -0.086396   

        NH3        CO       SO2        O3   Benzene   Toluene  Xylene  \
0 -0.008588  0.770963  0.238944 -0.249612  0.264279  0.208517     0.0   
1  0.293168  0.671077 -0.035333  0.052416  0.183153  0.187007     0.0   
2  0.037991  0.918452 -0.013726  0.257742  0.343649  0.457593     0.0   
3  0.089075  0.959977  0.201922  0.225972  0.042688 -0.053902     0.0   
4  0.218881  1.057755  0.228192  0.229598  0.269988 -0.152285     0.0   

        AQI  AQI_Bucket        Day                          Date  
0  0.030681    2.794393  16.196262 2015-01-16 04:42:37.009345792  
1  0.276605    2.413265  14.500000 2015-02-14 12:00:00.000000000  
2  0.250786    2.211982  16.000000 2015-03-16 00:00:00.000000000  
3  0.296833    2.085714  15.500000 2015-04-15 12:00:00.000000000  
4  0.323833    2.193548  16.000000 2015-05-16 00:00:00.000000000  

   Year       City     PM2.5      PM10        NO       NO2       NOx  \
0  2015  11.512674  0.138603  0.111125 -0.053633 -0.182989  0.060780   
1  2016  12.687752  0.262115  0.070190 -0.039878  0.086431 -0.108315   
2  2017  13.192152  0.175568  0.063968 -0.085808  0.081692 -0.300019   
3  2018  13.261938  0.079767  0.155770  0.108936  0.227406  0.224387   
4  2019  13.127182 -0.099043 -0.038186  0.059895 -0.015154  0.108187   

        NH3        CO       SO2        O3   Benzene   Toluene  Xylene  \
0  0.235161  0.710767 -0.151728 -0.084679 -0.018131 -0.047299     0.0   
1  0.197141  0.064313 -0.260322  0.078213 -0.047449 -0.085234     0.0   
2  0.062556 -0.448943 -0.210662 -0.039659 -0.253313 -0.314022     0.0   
3 -0.026822  0.093519  0.174511  0.042125  0.072524  0.127385     0.0   
4 -0.139493  0.052647  0.163861 -0.047343  0.091275  0.164949     0.0   

        AQI  AQI_Bucket     Month        Day                          Date  
0  0.252075    2.433417  6.788290  15.761871 2015-07-10 00:34:26.690467584  
1  0.215384    2.541691  6.671363  15.761070 2016-07-06 07:32:32.156411648  
2  0.059469    2.625507  7.000213  15.874174 2017-07-16 13:38:07.140115200  
3  0.091118    2.320507  6.592490  15.738062 2018-07-04 00:50:04.172461824  
4 -0.053913    2.152699  6.821246  15.779345 2019-07-11 01:11:10.104754176  

Weekly, Monthly, and Yearly AQI data saved successfully!
Out[38]:
0    0.000000
1   -0.327402
2   -0.327402
3   -0.327402
4   -0.327402
Name: ARIMA_Predicted_AQI, dtype: float64
MAE: 0.2340
MSE: 0.1180
RMSE: 0.3435
R² Score: 0.8820

Best Ridge Alpha: 10
Best Lasso Alpha: 0.01
Model: Ridge, MAE: 0.2217, MSE: 0.1163, RMSE: 0.3411, R² Score: 0.8820
Model: Lasso, MAE: 0.2210, MSE: 0.1164, RMSE: 0.3412, R² Score: 0.8819
Model: RandomForest, MAE: 0.1616, MSE: 0.0838, RMSE: 0.2896, R² Score: 0.9150
Model: GradientBoosting, MAE: 0.1764, MSE: 0.0877, RMSE: 0.2961, R² Score: 0.9111
Out[42]:
RandomForestRegressor(random_state=42)
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RandomForestRegressor(random_state=42)
ERROR: Could not find a version that satisfies the requirement pickle (from versions: none)
ERROR: No matching distribution found for pickle

[notice] A new release of pip is available: 24.0 -> 25.0
[notice] To update, run: python.exe -m pip install --upgrade pip

Model saved successfully!

Requirement already satisfied: plotly in c:\users\vinit solanki\onedrive\documents\vesit files\aqi_prediction\myenv\lib\site-packages (6.0.0)
Requirement already satisfied: narwhals>=1.15.1 in c:\users\vinit solanki\onedrive\documents\vesit files\aqi_prediction\myenv\lib\site-packages (from plotly) (1.25.2)
Requirement already satisfied: packaging in c:\users\vinit solanki\onedrive\documents\vesit files\aqi_prediction\myenv\lib\site-packages (from plotly) (24.2)

[notice] A new release of pip is available: 24.0 -> 25.0
[notice] To update, run: python.exe -m pip install --upgrade pip